Open data, transparency and accountability


Topic guide by Liz Carolan: “…introduces evidence and lessons learned about open data, transparency and accountability in the international development context. It discusses the definitions, theories, challenges and debates presented by the relationship between these concepts, summarises the current state of open data implementation in international development, and highlights lessons and resources for designing and implementing open data programmes.

Open data involves the release of data so that anyone can access, use and share it. The Open DataCharter (2015) describes six principles that aim to make data easier to find, use and combine:

  • open by default
  • timely and comprehensive
  • accessible and usable
  • comparable and interoperable
  • for improved governance and citizen engagement
  • for inclusive development and innovation

One of the main objectives of making data open is to promote transparency.

Transparency is a characteristic of government, companies, organisations and individuals that are open in the clear disclosure of information, rules, plans, processes and actions. Trans­parency of information is a crucial part of this. Within a development context, transparency and accountability initiatives have emerged over the last decade as a way to address developmental failures and democratic deficits.

There is a strong intersection between open data and transparency as concepts, yet as fields of study and practice, they have remained somewhat separate. This guide draws extensively on analysis and evidence from both sets of literature, beginning by outlining the main concepts and the theories behind the relationships between them.

Data release and transparency are parts of the chain of events leading to accountability.  For open data and transparency initiatives to lead to accountability, the required conditions include:

  • getting the right data published, which requires an understanding of the politics of data publication
  • enabling actors to find, process and use information, and to act on any outputs, which requires an accountability ecosystem that includes equipped and empowered intermediaries
  • enabling institutional or social forms of enforceability or citizens’ ability to choose better services,which requires infrastructure that can impose sanctions, or sufficient choice or official support for citizens

Programmes intended to increase access to information can be impacted by and can affect inequality. They can also pose risks to privacy and may enable the misuse of data for the exploitation of individuals and markets.

Despite a range of international open data initiatives and pressures, developing countries are lagging behind in the implementation of reforms at government level, in the overall availability of data, and in the use of open data for transparency and accountability. What is more, there are signs that ‘open-washing’ –superficial efforts to publish data without full integration with transparency commitments – may be obscuring backsliding in other aspects of accountability.

The topic guide pulls together lessons and guidance from open data, transparency and accountability work,including an outline of technical and non-technical aspects of implementing a government open data initiative. It also lists further resources, tools and guidance….(More)”

Crowdsourced map of safe drinking water


Springwise: “Just over two years ago, in April 2014, city officials in Flint, Michigan decided to save costs by switching the city’s water supply from Lake Huron to the Flint River. Because of the switch, residents of the town and their children were exposed to dangerous levels of lead. Much of the population suffered from the side effects of lead poisoning, including skin lesions, hair loss, depression and anxiety and in severe cases, permanent brain damage. Media attention, although focussed at first, inevitably died down. To avoid future similar disasters, Sean Montgomery, a neuroscientist and the CEO of technology company, Connected Future Labs, set up CitizenSpring.

CitizenSpring is an app which enables individuals to test their water supply using readily available water testing kits. Users hold a test strip underneath running water, hold the strip to a smartphone camera and press the button. The app then reveals the results of the test, also cataloguing the test results and storing them in the cloud in the form of a digital map. Using what Montgomery describes as “computer vision,” the app is able to detect lead levels in a given water source and confirm whether they exceed the Environmental Protection Agency’s “safe” threshold. The idea is that communities can inform themselves about their own and nearby water supplies in order that they can act as guardians of their own health. “It’s an impoverished data problem,” says Montgomery. “We don’t have enough data. By sharing the results of test[s], people can, say, find out if they’re testing a faucet that hasn’t been tested before.”

CitizenSpring narrowly missed its funding target on Kickstarter. However, collective monitoring can work. We have already seen the power of communities harnessed to crowdsource pollution data in the EU and map conflict zones through user-submitted camera footage….(More)”

When Innovation Goes Wrong


Christian Seelos & Johanna Mair at Stanford Social Innovation Review: “Efforts by social enterprises to develop novel interventions receive a great deal of attention. Yet these organizations often stumble when it comes to turning innovation into impact. As a result, they fail to achieve their full potential. Here’s a guide to diagnosing and preventing several “pathologies” that underlie this failure….

The core purpose of an innovation process is the conversion of uncertainty into knowledge. Or to put it another way: Innovation is essentially a matter of learning. In fact, one critical insight that we have drawn from our research is that effective organizations approach innovation not with an expectation of success but with an expectation of learning. Innovators who expect success from innovation efforts will inevitably encounter disappointment, and the experience of failure will generate a blame culture in their organization that dramatically lowers their chance of achieving positive impact. But a focus on learning creates a sense of progress rather than a sense of failure. The high-impact organizations that we have studied owe much of their success to their wealth of accumulated knowledge—knowledge that often has emerged from failed innovation efforts.

 

Innovation uncertainty has multiple dimensions, and organizations need to be vigilant about addressing uncertainty in all of its forms. (See “Types of Innovation Uncertainty” below.) Let’s take a close look at three aspects of the innovation process that often involve a considerable degree of uncertainty.

Problem formulation | Organizations may incorrectly frame the problem that they aim to solve, and identifying that problem accurately may require several iterations and learning cycles…

Solution development | Even when an organization has an adequate understanding of a problem, it may not be able to access and deploy the resources needed to create an effective and robust solution….

Alignment with identity | Innovation may lead an organization in a direction that does not fit its culture or its sense of its purpose—its sense of “who we are.”…

In short, innovation plus scaling equals impact. Innovation is an investment of resources that creates a new potential; scaling creates impact by enacting that potential. Because innovation creates only the potential for impact, we advocate replacing the assumption that “innovation is good, and more is better” with a more critical view: Innovation, we argue, needs to prove itself on the basis of the impact that it actually creates. The goal is not innovation for its own sake but productive innovation.

Productive innovation depends on two factors: (1) an organization’s capacity for efficiently replacing innovation uncertainty with knowledge, and (2) its ability to scale up innovation outcomes by enhancing its organizational effectiveness. Innovation and scaling thus work together to form an overall social impact creation process. Over time, an investment in innovation—in the work of overcoming uncertainty—yields positive social impact, and the value of such impact will eventually exceed the cost of that investment. But that will be the case only if an organization is able to master the scaling part of this process….

 

 

Focusing on Pathologies

Through our study of social enterprises, we have devised a set of six pathologies—six ways that organizations limit their capacity for productive innovation. From the stage when people first develop (or fail to develop) the idea for an innovation to the stage when scaling efforts take off (or fail to take off), these pathologies adversely affect an organization’s ability to make its way through the social impact creation process. (See “Creating Social Impact: Six Innovation Pathologies to Avoid” below.) Organizations can greatly improve the impact of their innovation efforts by working to prevent or treat these pathologies.

Never getting started | In too many cases, organizations simply fail to invest seriously in the work of innovation. This pathology has many causes. People in organizations may have neither the time nor the incentive to develop or communicate new ideas. Or they may find that their ideas fall on deaf ears. Or they may have a tendency to discuss an idea endlessly—until the problem that gave rise to it has been replaced by another urgent problem or until an opportunity has vanished….

Pursuing too many bad ideas | Organizations in the social sector frequently fall into the habit of embracing a wide variety of ideas for innovation without regard to whether those ideas are sound. The recent obsession with “scientific” evaluation tools such as randomized controlled trials, or RCTs, exemplifies this tendency to favor costly ideas that may or may not deliver real benefits. As with other pathologies, many factors potentially contribute to this one. Funders may push their favorite solutions regardless of how well they understand the problems that those solutions target or how well a solution fits a particular organization. Or an organization may fail to invest in learning about the context of a problem before adopting a solution. Wasting scarce resources on the pursuit of bad ideas creates frustration and cynicism within an organization. It also increases innovation uncertainty and the likelihood of failure….

Stopping too early | In some instances, organizations are unable or unwilling to devote adequate resources to the development of worthy ideas. When resources are scarce and not formally dedicated to innovation processes, project managers will struggle to develop an idea and may have to abandon it prematurely. Too often, they end up taking the blame for failure, and others in their organization ignore the adverse circumstances that caused it. Decision makers then reallocate resources on an ad-hoc basis to other urgent problems or to projects that seem more important. As a result, even promising innovation efforts come to a grinding halt….

Stopping too late | Even more costly than stopping too early is stopping too late. In this pathology, an organization continues an innovation project even after the innovation proves to be ineffective or unworkable. This problem occurs, for example, when an unsuccessful innovation happens to be the pet project of a senior leader who has limited experience. Leaders who have recently joined an organization and who are keen to leave their mark rather than continue what their predecessor has built are particularly likely to engage in this pathology. Another cause of “stopping too late” is the assumption that a project budget needs to be spent. The consequences of this pathology are clear: Organizations expend scarce resources with little hope for success and without gaining any useful knowledge….

Scaling too little | To repeat an essential point that we made earlier: no scaling, no impact. This pathology—which involves a failure to move beyond the initial stages of developing, launching, and testing an intervention—is all too common in the social enterprise field. Thousands of inspired young people want to become social entrepreneurs. But few of them are willing or able to build an organization that can deliver solutions at scale. Too many organizations, therefore, remain small and lack the resources and capabilities required for translating innovation into impact….

Innovating again too soon | Too many organizations rush to launch new innovation projects instead of investing in efforts to scale interventions that they have already developed. The causes of this pathology are fairly well known: People often portray scaling as dull, routine work and innovation as its more attractive sibling. “Innovative” proposals thus attract funders more readily than proposals that focus on scaling. Reinforcing this bias is the preference among many funders for “lean projects” that reduce overhead costs to a minimum. These factors lead organizations to jump opportunistically from one innovation grant to another….(More)”

Ideas to help civil servants understand the opportunities of data


, at Gov.UK: “Back in April we set out our plan for the discovery phase for what we are now calling “data science literacy”. We explained that we were going to undertake user research with civil servants to understand how they use data. The discovery phase has helped clarify the focus of this work, and we have now begun to develop options for a data science literacy service for government.

Discovery has helped us understand what we really mean when we say ‘data literacy’. For one person it can be a basic understanding of statistics, but to someone else it might mean knowledge of new data science approaches. But on the basis of our exploration, we have started to use the term “data science literacy” to mean the ability to understand how new data science techniques and approaches can be applied in real world contexts in the civil service, and to distinguish it from a broader definition of ‘data literacy’….

In the spirit of openness and transparency we are making this long list of ideas available here:

Data science driven apps

One way in which civil servants could come to understand the opportunities of data science would be to experience products and services which are driven by data science in their everyday roles. This could be something like having a recommendation engine for actions provided to them on the basis of information already held on the customer.

Sharing knowledge across government

A key user need from our user research was to understand how others had undertaken data science projects in government. This could be supported by something like a series of videos / podcasts created by civil servants, setting out case studies and approaches to data science in government. Alternatively, we could have a regularly organised speaker series where data science projects across government are presented alongside outside speakers.

Support for using data science in departments

Users in departments need to understand and experience data science projects in government so that they can undertake their own. Potentially this could be achieved through policy, analytical and data science colleagues working in multidisciplinary teams. Colleagues could also be supported by tools of differing levels of complexity ranging from a simple infographic showing at a high level the types of data available in a department to an online tool which diagnoses which approach people should take for a data science project on the basis of their aims and the data available to them.

In practice training

Users could learn more about how to use data science in their jobs by attending more formal training courses. These could take the form of something like an off-site, week-long training course where they experience the stages of undertaking a data science project (similar to the DWP Digital Academy). An alternative model could be to allocate one day a week to work on a project with departmental importance with a data scientist (similar to theData Science Accelerator Programme for analysts).

IMG_1603

Cross-government support for collaboration

For those users who have responsibility for leading on data science transformation in their departments there is also a need to collaborate with others in similar roles. This could be achieved through interventions such as a day-long unconference to discuss anything related to data science, and using online tools such as Google Groups, Slack, Yammer, Trello etc. We also tested the idea of a collaborative online resource where data science leads and others can contribute content and learning materials / approaches.

This is by no means an exhaustive list of potential ways to encourage data science thinking by policy and delivery colleagues across government. We hope this list is of interest to others in the field and we will update in the next six months about the transition of this project to Alpha….(More)”

Civil Solutions


Situation vacant: technology triathletes wanted


Anne-Marie Slaughter in the Financial Times: “It is time to celebrate a new breed of triathletes, who work in technology. When I was dean in the public affairs school at Princeton, I would tell students to aim to work in the public, private and civic sectors over the course of their careers.

Solving public problems requires collaboration among government, business and civil society. Aspiring problem solvers need the culture and language of all three sectors and to develop a network of contacts in each.

The public problems we face, in the US and globally, require lawyers, economists and issue experts but also technologists. A lack of technologists capable of setting up HealthCare.gov, a website designed to implement the Affordable Care act, led President Barack Obama to create the US Digital Service, which deploys Swat tech teams to address specific problems in government agencies.

But functioning websites that deliver government services effectively are only the most obvious technological need for the public sector.

Government can reinvent how it engages with citizens entirely, for example by personalising public education with digital feedback or training jobseekers. But where to find the talent? The market for engineers, designers and project managers sees big tech companies competing for graduates from the world’s best universities.

Governments can offer only a fraction of those salaries, combined with a rigid work environment, ingrained resistance to innovation and none of the amenities and perks so dear to Silicon Valley .

Government’s comparative advantage, however, is mission and impact, which is precisely what Todd Park sells…Still, demand outstrips supply. ….The goal is to create an ecosystem for public interest technology comparable to that in public interest law. In the latter, a number of American philanthropists created role models, educational opportunities and career paths for aspiring lawyers who want to change the world.

That process began in the 1960s, and today every great law school has a public interest programme with scholarships for the most promising students. Many branches of government take on top law school graduates. Public interest lawyers coming out of government find jobs with think-tanks and advocacy organisations and take up research fellowships, often at the law schools that educated them. When they need to pay the mortgage or send their kids to college, they can work at large law firms with pro bono programmes….We need much more. Every public policy school at a university with a computer science, data science or technology design programme should follow suit. Every think-tank should also become a tech tank. Every non-governmental organisation should have at least one technologist on staff. Every tech company should have a pro bono scheme rewarding public interest work….(More)”

25 Years Later, What Happened to ‘Reinventing Government’?


 at Governing: “…A generation ago, governments across the United States embarked on ambitious efforts to use performance measures to “reinvent” how government worked. Much of the inspiration for this effort came from the bestselling 1992 book Reinventing Government: How the Entrepreneurial Spirit Is Transforming the Public Sector by veteran city manager Ted Gaebler and journalist David Osborne. Gaebler and Osborne challenged one of the most common complaints about public administration — that government agencies were irredeemably bureaucratic and resistant to change. The authors argued that that need not be the case. Government managers and employees could and should, the authors wrote, be as entrepreneurial as their private-sector counterparts. This meant embracing competition; measuring outcomes rather than inputs or processes; and insisting on accountability.

For public-sector leaders, Gaebler and Osborne’s book was a revelation. “I would say it has been the most influential book of the past 25 years,” says Robert J. O’Neill Jr., the executive director of the International City/County Management Association (ICMA). At the federal level, Reinventing Government inspired Vice President Al Gore’s National Performance Review. But it had its greatest impact on state and local governments. Public-sector officials across the country read Reinventing Government and ingested its ideas. Osborne joined the consulting firm Public Strategies Group and began hiring himself out as an adviser to governments.

There’s no question states and localities function differently today than they did 25 years ago. Performance management systems, though not universally beloved, have become widespread. Departments and agencies routinely measure customer satisfaction. Advances in information technology have allowed governments to develop and share outcomes more easily than ever before. Some watchdog groups consider linking outcomes to budgets — also known as performance-based budgeting — to be a best practice. Government executives in many places talk about “innovation” as if they were Silicon Valley executives. This represents real, undeniable change.

Yet despite a generation of reinvention, government is less trusted than ever before. Performance management systems are sometimes seen not as an instrument of reform but as an obstacle to it. Performance-based budgeting has had successes, but they have rarely been sustained. Some of the most innovative efforts to improve government today are pursuing quite different approaches, emphasizing grassroots employee initiatives rather than strict managerial accountability. All of this raises a question: Has the reinventing government movement left a legacy of greater effectiveness, or have the systems it generated become roadblocks that today’s reformers must work around?  Or is the answer somehow “yes” to both of those questions?

Reinventing Government presented dozens of examples of “entrepreneurial” problem-solving, organized into 10 chapters. Each chapter illustrated a theme, such as results-oriented government or enterprising government. This structure — concrete examples grouped around larger themes — reflected the distinctive sensibilities of each author. Gaebler, as a city manager, had made a name for himself by treating constraints such as funding shortfalls or bureaucratic rules as opportunities. His was a bottom-up, let-a-hundred-flowers-bloom sensibility. He wanted his fellow managers to create cultures where risks could be taken and initiative could be rewarded.

Osborne, a journalist, was more of a systematizer, drawn to sweeping ideas. In his previous book, Laboratories of Democracy, he had profiled six governors who he believed were developing new approaches for delivering services that constituted a “third way” between big government liberalism and anti-government conservatism.Reinventing Government suggested how that would work in practice. It also offered readers a daring and novel vision of what government’s core mission should be. Government, the book argued, should focus less on operating programs and more on overseeing them. Instead of “rowing” (stressing administrative detail), senior public officials should do more “steering” (concentrating on overall strategy). They should contract out more, embrace competition and insist on accountability. This aspect of Osborne’s thinking became more pronounced as time went by.

“Today we are well beyond the experimental approach,” Osborne and Peter Hutchinson, a former Minnesota finance commissioner, wrote in their 2004 book, The Price of Government: Getting the Results We Need in an Age of Permanent Fiscal Crisis. A decade of experience had produced a proven set of strategies, the book continued. The foremost should be to turn the budget process “on its head, so that it starts with the results we demand and the price we are willing to pay rather than the programs we have and the costs they incur.” In other words, performance-based budgeting. Then, they continued, “we must cut government down to its most effective size and shape, through strategic reviews, consolidation and reorganization.”

Assessing the influence and efficacy of these ideas is difficult. According to the U.S. Census, the United States has 90,106 state and local governments. Tens of thousands of public employees read Reinventing Government and the books that followed. Surveys have shown that the use of performance measurement systems is widespread across state, county and municipal government. Yet only a handful of studies have sought to evaluate systematically the impact of Reinventing Government’s core ideas. Most have focused on just one, the idea highlighted in The Price of Government: budgeting for outcomes.

To evaluate the reinventing government movement primarily by assessing performance-based budgeting might seem a bit narrow. But paying close attention to the budgeting process is the key to understanding the impact of the entire enterprise. It reveals the difficulty of sustaining even successful innovations….

“Reinventing government was relatively blind to the role of legislatures in general,” says University of Maryland public policy professor and Governing columnist Donald F. Kettl. “There was this sense that the real problem was that good people were trapped in a bad system and that freeing administrators to do what they knew how to do best would yield vast improvements. What was not part of the debate was the role that legislatures might have played in creating those constraints to begin with.”

Over time, a pattern emerged. During periods of crisis, chief executives were able to implement performance-based budgeting. Often, it worked. But eventually legislatures pushed back….

There was another problem. Measuring results, insisting on accountability — these were supposed to spur creative problem-solving. But in practice, says Blauer, “whenever the budget was invoked in performance conversations, it automatically chilled innovative thinking; it chilled engagement,” she says. Agencies got defensive. Rather than focusing on solving hard problems, they focused on justifying past performance….

The fact that reinventing government never sparked a revolution puzzles Gaebler to this day. “Why didn’t more of my colleagues pick it up and run with it?” he asks. He thinks the answer may be that many public managers were simply too risk-averse….(More)”.

“Data-Driven Policy”: San Francisco just showed us how it should work.


abhi nemani at Medium: “…Auto collisions with bikes (and also pedestrians) poses a real threat to the safety and wellbeing of residents. But more than temporary injuries, auto collisions with bikes and pedestrians can kill people. And it does at an alarming rate. According to the city, “Every year in San Francisco, about 30 people lose their lives and over 200 more are seriously injured while traveling on city streets.”…

Problem -> Data Analysis

The city government, in good fashion, made a commitment to do something about. But in better fashion, they decided to do so in a data-driven way. And they tasked the Department of Public Health in collaboration with theDepartment of Transportation to develop policy. What’s impressive is that instead of some blanket policy or mandate, they opted to study the problem,take a nuanced approach, and put data first.

SF High Injury Network

The SF team ran a series of data-driven analytics to determine the causes of these collisions. They developed TransBase to continuously map and visualize traffic incidents throughout the city. Using this platform, then, they developed the “high injury network” — they key places where most problems happen; or as they put it, “to identify where the most investments in engineering, education and enforcement should be focused to have the biggest impact in reducing fatalities and severe injuries.” Turns out that, just12 percent of intersections result in 70% of major injuries. This is using data to make what might seem like an intractable problem, tractable….

Data Analysis -> Policy

So now what? Well, this month, Mayor Ed Lee signed an executive directive to challenge the city to implement these findings under the banner of“Vision Zero”: a goal of reducing auto/pedestrian/bike collision deaths to zero by 2024….

Fortunately, San Francisco took the next step: they put their data to work.

Policy -> Implementation

This week, the city of San Francisco announced plans to build its first“Protected Intersection”:

“Protected intersections use a simple design concept to make everyone safer.Under this configuration, features like concrete islands placed at the cornersslow turning cars and physically separate people biking and driving. They alsoposition turning drivers at an angle that makes it easier for them to see andyield to people walking and biking crossing their path.”

That’s apparently just the start: plans are underway for other intersections,protected bike lanes, and more. Biking and walking in San Francisco is about to become much safer. (Though maybe not easier: the hills — they’rethe worst.)

***

There is ample talk of “Data-Driven Policy” — indeed, I’ve written about it myself — but too often we get lost in the abstract or theoretical….(More)”

How civic intelligence can teach what it means to be a citizen


 at the Conversation: “This political season, citizens will be determining who will represent them in the government. This, of course, includes deciding who will be the next president, but also who will serve in thousands of less prominent positions.

But is voting the only job of a citizen? And if there are others, what are they? Who decides who will do the other jobs – and how they should be done?

The concept of “civic intelligence” tries to address such questions.

I’ve been researching and teaching the concept of “civic intelligence” for over 15 years. Civic intelligence can help us understand how decisions in democratic societies are made now and, more importantly, how they could be made in the future.

For example, my students and I used civic intelligence as the focus for comparing colleges and universities. We wanted to see how well schools helped educate their students for civic engagement and social innovation and how well the schools themselves supported this work within the broader community.

My students also practiced civic intelligence, as the best way of learning it is through “real world” projects such as developing a community garden at a high school for incarcerated youth….

The term “civic intelligence” was first used in English in 1898 by an American clergyman Josiah Strong in his book “The Twentieth Century City” when he wrote of a “dawning social self-consciousness.”

Untold numbers of people have been thinking and practicing civic intelligence without using the term. …There are more contemporary approaches as well. These include:

  • Sociologist Xavier de Souza Briggs’ research on how people from around the world have integrated the efforts of civil society, grassroots organizations and government to create sustainable communities.
  • With a slightly different lens, researcher Jason Corburn has examined how “ordinary” people in economically underprivileged neighborhoods have used “Street Science” to understand and reduce disease and environmental degradation in their communities.
  • Elinor Ostrom, recently awarded the Nobel Prize in economics, has studied how groups of people from various times and places managed resources such as fishing grounds, woodlots and pastures by working together collectively to preserve the livelihoods’ sources for future generations.

Making use of civic intelligence

Civic intelligence is generally an attribute of groups. It’s a collective capability to think and work together.

Advocates and practitioners of civic intelligence (as well as many others) note that the risks of the 21st century, which include climate change, environmental destruction and overpopulation, are quantitatively and qualitatively unlike the risks of prior times. They hypothesize that these risks are unlikely to be addressed satisfactorily by government and other leaders without substantial citizen engagement….

At a basic level, “governance” happens when neighborhood groups, nonprofit organizations or a few friends come together to help address a shared concern.

Their work can take many forms, including writing, developing websites, organizing events or demonstrations, petitioning, starting organizations and, even, performing tasks that are usually thought of as “jobs for the government.”

And sometimes “governance” could even mean breaking some rules, possibly leading to far-reaching reforms. For example, without civil disobedience, the U.S. might still be a British colony. And African-Americans might still be forced to ride in the back of the bus.

As a discipline, civic intelligence provides a broad focus that incorporates ideas and findings from many fields of study. It involves people from all walks of life, different cultures and circumstances.

A focus on civic intelligence could lead directly to social engagement. I believe understanding civic intelligence could help address the challenges we must face today and tomorrow….(More)”

Encouraging and Sustaining Innovation in Government: Technology and Innovation in the Next Administration


New report by Beth Simone Noveck and Stefaan Verhulst: “…With rates of trust in government at an all-time low, technology and innovation will be essential to achieve the next administration’s goals and to deliver services more effectively and efficiently. The next administration must prioritize using technology to improve governing and must develop plans to do so in the transition… This paper provides analysis and a set of concrete recommendations, both for the period of transition before the inauguration, and for the start of the next presidency, to encourage and sustain innovation in government. Leveraging the insights from the experts who participated in a day-long discussion, we endeavor to explain how government can improve its use of using digital technologies to create more effective policies, solve problems faster and deliver services more effectively at the federal, state and local levels….

The broad recommendations are:

  • Scale Data Driven Governance: Platforms such as data.gov represent initial steps in the direction of enabling data-driven governance. Much more can be done, however, to open-up data and for the agencies to become better consumers of data, to improve decision-making and scale up evidence-based governance. This includes better use of predictive analytics, more public engagement; and greater use of cutting-edge methods like machine learning.
  • Scale Collaborative Innovation: Collaborative innovation takes place when government and the public work together, thus widening the pool of expertise and knowledge brought to bear on public problems. The next administration can reach out more effectively, not just to the public at large, but to conduct targeted outreach to public officials and citizens who possess the most relevant skills or expertise for the problems at hand.
  • Promote a Culture of Innovation: Institutionalizing a culture of technology-enabled innovation will require embedding and institutionalizing innovation and technology skills more widely across the federal enterprise. For example, contracting, grants and personnel officials need to have a deeper understanding of how technology can help them do their jobs more efficiently, and more people need to be trained in human-centered design, gamification, data science, data visualization, crowdsourcing and other new ways of working.
  • Utilize Evidence-Based Innovation: In order to better direct government investments, leaders need a much better sense of what works and what doesn’t. The government spends billions on research in the private and university sectors, but very little experimenting with, testing, and evaluating its own programs. The next administration should continue developing an evidence-based approach to governance, including a greater use of methods like A/B testing (a method of comparing two versions of a webpage or app against each other to determine which one performs the best); establishing a clearinghouse for success and failure stories and best practices; and encouraging overseers to be more open to innovation.
  • Make Innovation a Priority in the Transition: The transition period represents a unique opportunity to seed the foundations for long-lasting change. By explicitly incorporating innovation into the structure, goals and activities of the transition teams, the next administration can get a fast start in implementing policy goals and improving government operations through innovation approaches….(More)”